STUDY OBJECTIVES A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed-night shift workers. METHODS A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (&10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and a prototype assessment tool are available open source. RESULTS Model predictions of DLMO showed good concordance with in-lab DLMO, with a Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 hours, with 76% and 91% of the predictions falling with 2 and 4 hours, respectively. CONCLUSION This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.

中文翻译：

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使用可穿戴技术预测昼夜节律失调：夜班工人腕戴式活动记录仪和光度测量的验证

研究目标 成功治疗轮班工人昼夜节律失调的一个关键障碍是确定临床或现场环境中的昼夜节律阶段。从腕部活动记录仪被动收集的光和运动数据可以通过数学模型预测昼夜节律；然而，这些模型在很大程度上已经在非轮班工作的成年人身上进行了测试。这项研究测试了活动记录在预测固定夜班工人昏暗的褪黑激素发作 (DLMO) 方面的可行性和准确性。方法 45 名夜班工人在实验室完成 DLMO 之前佩戴腕部活动记录仪（17.0 天 ± 10.3 SD）。DLMO 通过 24 小时在昏暗光线 (&10 lux) 下的唾液样本进行评估。来自活动记录的数据被提供为数学模型的输入，以生成昼夜节律相位的预测。作为 DLMO 的代理，对一致性进行评估并与非工作日的平均睡眠时间进行比较。模型代码和原型评估工具都是开源的。结果 DLMO 的模型预测与实验室内 DLMO 显示出良好的一致性，Lin 的一致性系数为 0.70，是使用平均睡眠时间作为 DLMO 代理的一致性的两倍。预测的绝对平均误差为 2.88 小时，其中 76% 和 91% 的预测分别在 2 和 4 小时内下降。结论 本研究首次证明了使用基于腕部活动描记法的昼夜节律相位估计作为临床上有用且有效的替代固定夜班工人 DLMO 的实验室测量方法。未来的研究应该探索额外的预测因素如何影响准确性。